




import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil




class 						UpperCamelCase__      :

    '''simple docstring'''



    def __init__(       self	,    UpperCamelCase__ = "cpu"	,    UpperCamelCase__ = "openai/clip-vit-large-patch14"	)	-> Optional[int]:
           lowerCamelCase  : Any     					=   device
           lowerCamelCase  : Tuple     					=   CLIPTokenizerFast.from_pretrained(lowerCamelCase_	)
           lowerCamelCase  : Union[str, Any]     					=   [0.48145466, 0.4578275, 0.40821073]
           lowerCamelCase  : Optional[Any]     					=   [0.26862954, 0.26130258, 0.27577711]
           lowerCamelCase  : List[Any]     					=   torchvision.transforms.Normalize(self.image_mean	,    self.image_std	)
           lowerCamelCase  : Union[str, Any]     					=   torchvision.transforms.Resize(224	)
           lowerCamelCase  : str     					=   torchvision.transforms.CenterCrop(224	)







    def        _lowercase	(       self	,    UpperCamelCase__	)	-> Tuple:
           lowerCamelCase  : List[Any]     					=   self.resize(lowerCamelCase_	)
           lowerCamelCase  : List[Any]     					=   self.center_crop(lowerCamelCase_	)
           lowerCamelCase  : int     					=   self.normalize(lowerCamelCase_	)
           return images







    def __call__(       self	,    UpperCamelCase__=None	,    UpperCamelCase__=None	,    **UpperCamelCase__	)	-> Optional[int]:
           lowerCamelCase  : str     					=   self.tokenizer(text=lowerCamelCase_	,    **lowerCamelCase_	)
           lowerCamelCase  : str     					=   self.preprocess_img(lowerCamelCase_	)
           lowerCamelCase  : Dict     					=   {key: value.to(self.device	) for (key, value) in encoding.items()}
           return encoding




class 						UpperCamelCase__      (nn.Module	):

    '''simple docstring'''



    def __init__(       self	,    UpperCamelCase__=10	,    UpperCamelCase__=0.01	,    UpperCamelCase__=None	,    UpperCamelCase__=None	,    UpperCamelCase__=None	,    UpperCamelCase__=None	,    UpperCamelCase__=None	,    UpperCamelCase__=None	,    UpperCamelCase__=False	,    UpperCamelCase__=True	,    UpperCamelCase__="image"	,    UpperCamelCase__=True	,    UpperCamelCase__=False	,    UpperCamelCase__=False	,    UpperCamelCase__=False	,    )	-> Any:
           super().__init__()
           lowerCamelCase  : List[str]     					=   None
           lowerCamelCase  : str     					=   device if device else get_device()
           if vqgan:
                  lowerCamelCase  : Tuple     					=   vqgan
           else:
                  lowerCamelCase  : Union[str, Any]     					=   load_vqgan(self.device	,    conf_path=lowerCamelCase_	,    ckpt_path=lowerCamelCase_	)
           self.vqgan.eval()
           if clip:
                  lowerCamelCase  : int     					=   clip
           else:
                  lowerCamelCase  : Any     					=   CLIPModel.from_pretrained("openai/clip-vit-base-patch32"	)
           self.clip.to(self.device	)
           lowerCamelCase  : List[str]     					=   ProcessorGradientFlow(device=self.device	)

           lowerCamelCase  : Dict     					=   iterations
           lowerCamelCase  : Tuple     					=   lr
           lowerCamelCase  : Union[str, Any]     					=   log
           lowerCamelCase  : str     					=   make_grid
           lowerCamelCase  : List[Any]     					=   return_val
           lowerCamelCase  : Tuple     					=   quantize
           lowerCamelCase  : List[Any]     					=   self.vqgan.decoder.z_shape







    def        _lowercase	(       self	,    UpperCamelCase__=None	,    UpperCamelCase__=None	,    UpperCamelCase__=5	,    UpperCamelCase__=True	)	-> Union[str, Any]:
           lowerCamelCase  : str     					=   []
           if output_path is None:
                  lowerCamelCase  : Tuple     					=   "./animation.gif"
           if input_path is None:
                  lowerCamelCase  : List[Any]     					=   self.save_path
           lowerCamelCase  : Optional[int]     					=   sorted(glob(input_path + "/*"	)	)
           if not len(lowerCamelCase_	):
                  raise ValueError(
                      "No images found in save path, aborting (did you pass save_intermediate=True to the generate"
                      " function?)"	)
           if len(lowerCamelCase_	) == 1:
                  print("Only one image found in save path, (did you pass save_intermediate=True to the generate function?)"	)
           lowerCamelCase  : Union[str, Any]     					=   total_duration / len(lowerCamelCase_	)
           lowerCamelCase  : Union[str, Any]     					=   [frame_duration] * len(lowerCamelCase_	)
           if extend_frames:
                  lowerCamelCase  : Union[str, Any]     					=   1.5
                  lowerCamelCase  : Optional[int]     					=   3
           for file_name in paths:
                  if file_name.endswith(".png"	):
                         images.append(imageio.imread(lowerCamelCase_	)	)
           imageio.mimsave(lowerCamelCase_	,    lowerCamelCase_	,    duration=lowerCamelCase_	)
           print(F'''gif saved to {output_path}'''	)







    def        _lowercase	(       self	,    UpperCamelCase__=None	,    UpperCamelCase__=None	)	-> str:


           if not (path or img):
                  raise ValueError("Input either path or tensor"	)
           if img is not None:
                  raise NotImplementedError
           lowerCamelCase  : Union[str, Any]     					=   preprocess(Image.open(lowerCamelCase_	)	,    target_image_size=256	).to(self.device	)
           lowerCamelCase  : Optional[int]     					=   preprocess_vqgan(lowerCamelCase_	)
           lowerCamelCase					, *lowerCamelCase  : Optional[int]     					=   self.vqgan.encode(lowerCamelCase_	)
           return z







    def        _lowercase	(       self	,    UpperCamelCase__	)	-> int:
           lowerCamelCase  : int     					=   self.latent.detach().requires_grad_()
           lowerCamelCase  : List[str]     					=   base_latent + transform_vector
           if self.quantize:
                  lowerCamelCase					, *lowerCamelCase  : Union[str, Any]     					=   self.vqgan.quantize(lowerCamelCase_	)
           else:
                  lowerCamelCase  : Union[str, Any]     					=   trans_latent
           return self.vqgan.decode(lowerCamelCase_	)







    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__=None	)	-> Dict:
           lowerCamelCase  : List[str]     					=   self.clip_preprocessor(text=lowerCamelCase_	,    images=lowerCamelCase_	,    return_tensors="pt"	,    padding=lowerCamelCase_	)
           lowerCamelCase  : List[Any]     					=   self.clip(**lowerCamelCase_	)
           lowerCamelCase  : Optional[int]     					=   clip_outputs.logits_per_image
           if weights is not None:
                  lowerCamelCase  : Any     					=   similarity_logits * weights
           return similarity_logits.sum()







    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	)	-> Union[str, Any]:
           lowerCamelCase  : Union[str, Any]     					=   self._get_clip_similarity(pos_prompts["prompts"]	,    lowerCamelCase_	,    weights=(1 / pos_prompts["weights"])	)
           if neg_prompts:
                  lowerCamelCase  : int     					=   self._get_clip_similarity(neg_prompts["prompts"]	,    lowerCamelCase_	,    weights=neg_prompts["weights"]	)
           else:
                  lowerCamelCase  : Dict     					=   torch.tensor([1]	,    device=self.device	)
           lowerCamelCase  : Tuple     					=   -torch.log(lowerCamelCase_	) + torch.log(lowerCamelCase_	)
           return loss







    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	)	-> Optional[int]:
           lowerCamelCase  : Union[str, Any]     					=   torch.randn_like(self.latent	,    requires_grad=lowerCamelCase_	,    device=self.device	)
           lowerCamelCase  : List[str]     					=   torch.optim.Adam([vector]	,    lr=self.lr	)

           for i in range(self.iterations	):
                  optim.zero_grad()
                  lowerCamelCase  : Tuple     					=   self._add_vector(lowerCamelCase_	)
                  lowerCamelCase  : str     					=   loop_post_process(lowerCamelCase_	)
                  lowerCamelCase  : int     					=   self._get_CLIP_loss(lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_	)
                  print("CLIP loss"	,    lowerCamelCase_	)
                  if self.log:
                         wandb.log({"CLIP Loss": clip_loss}	)
                  clip_loss.backward(retain_graph=lowerCamelCase_	)
                  optim.step()
                  if self.return_val == "image":
                         yield custom_to_pil(transformed_img[0]	)
                  else:
                         yield vector







    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	)	-> List[str]:
           wandb.init(reinit=lowerCamelCase_	,    project="face-editor"	)
           wandb.config.update({"Positive Prompts": positive_prompts}	)
           wandb.config.update({"Negative Prompts": negative_prompts}	)
           wandb.config.update({"lr": self.lr, "iterations": self.iterations}	)
           if image_path:
                  lowerCamelCase  : str     					=   Image.open(lowerCamelCase_	)
                  lowerCamelCase  : Optional[Any]     					=   image.resize((256, 256)	)
                  wandb.log("Original Image"	,    wandb.Image(lowerCamelCase_	)	)







    def        _lowercase	(       self	,    UpperCamelCase__	)	-> Optional[int]:


           if not prompts:
                  return []
           lowerCamelCase  : List[str]     					=   []
           lowerCamelCase  : Optional[Any]     					=   []
           if isinstance(lowerCamelCase_	,    lowerCamelCase_	):
                  lowerCamelCase  : int     					=   [prompt.strip() for prompt in prompts.split("|"	)]
           for prompt in prompts:
                  if isinstance(lowerCamelCase_	,    (tuple, list)	):
                         lowerCamelCase  : Tuple     					=   prompt[0]
                         lowerCamelCase  : Any     					=   float(prompt[1]	)
                  elif ":" in prompt:
                         lowerCamelCase					, lowerCamelCase  : List[str]     					=   prompt.split(":"	)
                         lowerCamelCase  : Any     					=   float(lowerCamelCase_	)
                  else:
                         lowerCamelCase  : Optional[Any]     					=   prompt
                         lowerCamelCase  : str     					=   1.0
                  processed_prompts.append(lowerCamelCase_	)
                  weights.append(lowerCamelCase_	)
           return {
               "prompts": processed_prompts,
               "weights": torch.tensor(lowerCamelCase_	,    device=self.device	),
           }







    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__=None	,    UpperCamelCase__=None	,    UpperCamelCase__=True	,    UpperCamelCase__=False	,    UpperCamelCase__=True	,    UpperCamelCase__=True	,    UpperCamelCase__=None	,    )	-> Optional[int]:


           if image_path:
                  lowerCamelCase  : Dict     					=   self._get_latent(lowerCamelCase_	)
           else:
                  lowerCamelCase  : List[str]     					=   torch.randn(self.latent_dim	,    device=self.device	)
           if self.log:
                  self._init_logging(lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_	)

           assert pos_prompts, "You must provide at least one positive prompt."
           lowerCamelCase  : str     					=   self.process_prompts(lowerCamelCase_	)
           lowerCamelCase  : List[Any]     					=   self.process_prompts(lowerCamelCase_	)
           if save_final and save_path is None:
                  lowerCamelCase  : int     					=   os.path.join("./outputs/"	,    "_".join(pos_prompts["prompts"]	)	)
           if not os.path.exists(lowerCamelCase_	):
                  os.makedirs(lowerCamelCase_	)
           else:
                  lowerCamelCase  : Dict     					=   save_path + "_" + get_timestamp()
                  os.makedirs(lowerCamelCase_	)
           lowerCamelCase  : List[str]     					=   save_path

           lowerCamelCase  : Optional[int]     					=   self.vqgan.decode(self.latent	)[0]
           if show_intermediate:
                  print("Original Image"	)
                  show_pil(custom_to_pil(lowerCamelCase_	)	)

           lowerCamelCase  : List[str]     					=   loop_post_process(lowerCamelCase_	)
           for iter, transformed_img in enumerate(self._optimize_CLIP(lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_	)	):
                  if show_intermediate:
                         show_pil(lowerCamelCase_	)
                  if save_intermediate:
                         transformed_img.save(os.path.join(self.save_path	,    F'''iter_{iter:03d}.png'''	)	)
                  if self.log:
                         wandb.log({"Image": wandb.Image(lowerCamelCase_	)}	)
           if show_final:
                  show_pil(lowerCamelCase_	)
           if save_final:
                  transformed_img.save(os.path.join(self.save_path	,    F'''iter_{iter:03d}_final.png'''	)	)







import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Any:







 '''simple docstring'''
 # Initialise PyTorch model
 UpperCamelCase			  =      MobileBertConfig.from_json_file(UpperCamelCase_       )
 print(f"""Building PyTorch model from configuration: {config}"""       )
 UpperCamelCase			  =      MobileBertForPreTraining(UpperCamelCase_       )
 # Load weights from tf checkpoint
 UpperCamelCase			  =      load_tf_weights_in_mobilebert(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
 torch.save(model.state_dict()							,					UpperCamelCase_       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
     )
     parser.add_argument(
         """--mobilebert_config_file""",
         default=None,
         type=str,
         required=True,
         help=(
             """The config json file corresponding to the pre-trained MobileBERT model. \n"""
             """This specifies the model architecture."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     _SCREAMING_SNAKE_CASE 		=      parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
